Aging and Health Financing in the US: A General Equilibrium Analysis - - PowerPoint PPT Presentation
Aging and Health Financing in the US: A General Equilibrium Analysis Juergen Jung Chung Tran Towson University Australian National University Matthew Chambers Towson University Barcelona GSE Summer Forum, June 2016 Disclaimer This project
Juergen Jung Chung Tran Towson University Australian National University Matthew Chambers Towson University Barcelona GSE Summer Forum, June 2016
This project was supported by funds from the Centers for Medicare & Medicaid Services, Office of the Actuary (CMS/OACT). The content is solely the responsibility of the authors and does not represent the official views of the funding institutions.
2 4 6 8 10 12 in $1,000 20 30 40 50 60 70 80 90 Age
Other Worker's compensation State insurance Federal insurance Tricare CHAMPUS Veteran's benefits Private insurance Medicaid Medicare Out-of-pocket
Source: MEPS 1999-2009
2010 2020 2030 2040 2050 2060
Decade
5 10 15 20 25 30
%
Population > 65 (in % of Working Age Population)
Source: Boards of Trustees (2015)
The long-term fiscal outlook in the US
Sensitive to assumptions about how health care spending (CBO (2014)) Fiscal gap between 6.1 percent and 9.0 percent of GDP (Auerbach and Gale (2013))
CBO’s projections abstract from microfoundations of health spending and financing
Lifecycle profiles of health-related behavior Behavioral responses to demographic shift and policy reforms
1 Quantify the effects of population aging on healthcare spending and
financing in US
2 Assess the implications of the ACA reform in this aging context
A Bewley-Grossman model of health capital with heterogenous agents
idiosyncratic income and health shocks incomplete markets
Microfoundations of health-related behavior
demand for medical services and health insurance
The US institutional details:
Medicare and Medicaid Group-based (GHI) and Individual-based insurance (IHI)
Calibrate the model to US data before the ACA reform
Medical Expenditure Panel Survey Population projections by CMS/OACT
1 Without ACA: Aging leads to large increases in medical spending
↑ Health expenditures by 37 percent (2060 demographic structure) ↑ Medicare by 50 percent ↑ Insurance take-up for workers from 77 to 81 percent
2 Introduction of ACA
increases the fraction of insured workers
up to 99 percent expansion of Medicaid and IHI ACA stabilizes insurance take-up for all simulated periods
mitigates the increase in health expenditures
↓ health expenditures by 2 percent move uninsured workers into Medicaid
increases fiscal cost mainly via the expansion of Medicaid aging itself diminishes impact of ACA
1 Economics of aging
Wise (2005), Bloom, Canning and Fink (2010) and De la Croix (2013) for an overview Aging and fiscal policy:
Deterministic: Auerbach and Kotlikoff (1987), Faruqee (2002), Kotlikoff, Smetters and Walliser (2007) Stochastic: De Nardi, Imrohoroğlu and Sargent (1999), Braun and Joines (2015), Kitao (2015) and Nishiyama (2015)
2 Quantitative macroeconomics/public finance
Pioneers: Bewley (1986), Huggett (1993) and Aiyagari (1994) Health risk and precautionary savings: Kotlikoff (1988), Levin (1995), Hubbard, Skinner and Zeldes (1995) and Palumbo (1999). Large scale models with health shocks and health policy: Jeske and Kitao (2009), Pashchenko and Porapakkarm (2013), Janicki (2014), Kopecky and Koreshkova (2014), Capatina (2015)
3 Models explaining health spending within Macro frameworks:
Lifecycle models that analyze the determinants of rising health care cost in the US
Features: technological progress, economic growth and social security (Suen (2006), Hall and Jones (2007), Fonseca et al. (2013) and Zhao (2014))
This paper: extends our previous framework in Jung and Tran (2016)
a rich institutional framework and the ACA altering the demographic structure in the model to mimic the process of population aging the effects of aging on health care cost and health financing
Overlapping Generations (OLG) Model
Lifespan: age 20 to 90
Heterogeneous agents
Idiosyncratic shocks: labor productivity and health shocks Health as consumption and investment goods
Endogenous health spending Choice of private health insurance
Market structure: consumption goods, health care goods, capital, labor markets, and incomplete financial markets Fiscal policy: income tax, social security, health insurance, minimum consumption
Preferences: u (c, l, h) =
lj
1−ηκ
× h1−κ
1−σ
1 − σ Health capital: hj =
Investment
φjmξ
j
+
Trend
j
Disturbance
j
Human capital (“labor”): ej = e
j
Pr
j+1|ǫh j
j , Pr
j+1|ǫl j
j and Pr
j+1|ǫGHI j
j,ϑ
Private health insurance: group (GHI) or individual (IHI) Public (social) health insurance: Medicaid or Medicare Health insurance status: inj =
if No insurance, 1 if Individual health insurance IHI, 2 if Group health insurance GHI, 3 if Medicaid.
Agent’s out-of-pocket health expenditures depend on insurance state
pinj
m × mj,
if inj = 0 ρinj
m × mj
if inj > 0
Final goods C production sector for price pC = 1: max
{K, L} {F (K, L) − qK − wL}
Medical services M production sector for price pm: max
{Km, Lm} {pmFm (Km, Lm) − qKm − wLm}
pm is a base price for medical services Price paid by households depends on insurance state: pinj
j
=
pm
νinj is an insurance state dependent markup factor
Profits are redistributed to all surviving agents
t t+1
Shocks:
Shocks:
Choices:
State vector: "# = {&, (, ), ℎ, )+, ,-, ,., ,/01} "#3 = {& + 1, (′, ), ℎ′, )+′, ,′-, ,′., ,′#
/01}
Choice ={6, 7, 8, (, )+′}
Insurance companies GHI and IHI clear zero profit condition
Details
Government budget constraint clears
Details
Pension program financed via payroll tax
Details
Accidental bequests to surviving individuals
Details
1 Given the transition probability matrices and the exogeneous
government policies, a competitive equilibrium is a collection of sequences of distributions of household decisions, aggregate capital stocks of physical and human capital, and market prices such that Agents solve the consumer problem The F.O.Cs of firms hold The budget constraints of insurances companies hold All markets clear All government programs and the general budget clear The distribution is stationary
Competitive Equilibrium Details
Goal: to match U.S. data pre-ACA (before 2010) Data sources:
MEPS: labor supply, health shocks, health expenditures, coinsurance rates PSID: initial asset distribution CMS: demographic profiles Previous studies: income process, labor shocks, aggregates
Health capital accumulation: hj =
Investment
φjmξ
j
+
Trend
j
Disturbance
j
Health capital measure in MEPS: SF 12-v2 δh → MEPS|insured & 0-medical spenders → ¯ hj =
Trend
j
¯
hj−1 ǫh and Πh from MEPS
MEPS data split each cohort j into 4 risk groups Average health capital per risk group:
¯
hmax
j,d
> ¯ h3
j,d > ¯
h2
j,d > ¯
h1
j,d
ǫh
j =
¯ h3
j,d − ¯
hmax
j,d
¯ hmax
j,d
, ¯ h2
j,d − ¯
hmax
j,d
¯ hmax
j,d
, ¯ h1
j,d − ¯
hmax
j,d
¯ hmax
j,d
m
Assumption: Associate resulting health shock with risk group by age Non-parametric estimation of transition probabilities health shocks
Human Capital
Final goods production: F (K, L) = AK αL1−α Medical services production: Fm (Km, Lm) = AmK αm
m L1−αm m
Parameters from other studies A = 1 and Am calibrated to match aggregate health spending
Medicare/Medicaid reimbursement rates (to providers) are about 70%
Average price markup for uninsured around 60% (Brown (2006)) Large GHI can negotiate favorable prices (Phelps (2003)) Price vector:
m
, pIHI
m , pGHI m , pMaid m
, pMcare
m
More Calibration Details
20 30 40 50 60 70 80 90 Age 20 40 60 80 100 120 140
Retired: 65
[1] Med-Spend.
Data MEPS Data CMS Model
20 40 60 80 100
10 20 30 40 50 60 70 % [2] Med-Spend. Distr.
Data Model
50 60 70 80 90 100 110 Cumulative % 100 200 300 400 500 600
[3] Inv. CDF Med.Spend.
Data Model
25 30 35 40 45 50 55 60 Age 20 40 60 80 100 % [4] IHI %
Data Model
25 30 35 40 45 50 55 60 Age 20 40 60 80 100 % [5] GHI %
Data Model
25 30 35 40 45 50 55 60 Age 20 40 60 80 100 % [6] Medicaid %
Data Model
Source: MEPS 2000-2009
20 30 40 50 60 70 80 90 Age 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Normalized Log Assets [1] Assets
Data Model
20 30 40 50 60 70 80 90 Age 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Normalized Log Income [2] Income
Data Model
20 30 40 50 60 70 80 90 Age 0.0 0.2 0.4 0.6 0.8 1.0 Normalized Log Consumption [3] Consumption
Data Model
20 25 30 35 40 45 50 55 60 65 Age 18 20 22 24 26 28 30 32 34 Hours [4] Avge Labor Hours per Week
Data Model
Source: PSID 1984-2007 and CPS 1999-2009
20 40 60 80 100 120 140
Income in $1,000
1 2 3 4 5
frequency in %
FPL 133FPL 400FPL MaidFPL Maid133FPL Maid400FPL
[1] Income Distribution SS1 with FPL
Data Model 20 40 60 80 100 120 140
Wage in $1,000
1 2 3 4 5 6 7
frequency in % [2] Wage Distribution SS1 with FPL
Data Model
Moments Model Data Source
17.6% 17.07% CMS communication
6.7% 7.6% MEPS 1999/2009
62.2% 63.6% MEPS 1999/2009
9.0% 9.2% MEPS 1999/2009
2.9 2.6 − 3 NIPA
4.2% 4% NIPA
5.9% 5% OMB 2008
3.1% 2.5 − 3.1% U.S. Dept of Health 2007
9.4% 10 − 12% IRS
5.0% 5.7% Mendoza et al. (1994)
2.9% 1.5 − 2.9%
21.8% 28.3% Stephenson (1998)
see figure
see figure
see figure
1 Benchmark economy in 2010 →fix baseline parameters 2 Change the survival probabilities to match the 10-year average
demographic structure of CMS/OACT population forecasts for 2030, 2040, 2050, 2060
3 Each time fix the particular demographic structure of a given decennial
and resolve (using Benchmark paras) for a new steady
4 “Updating” the age profile essentially creates a larger share of older
individuals in the model by appropriately increasing individual survival probabilities
5 We do NOT solve for the transition path from 2010 to 2060!
30 40 50 60 70 80 90 Age 30 40 50 60 70 80 90 100 % [1] Survival Probabilities: 2010-2060
2010 2020 2030 2040 2050 2060
30 40 50 60 70 80 90 Age 2 4 6 8 10 12 % [2] Population Profiles: 2010-2060
2010 2020 2030 2040 2050 2060
Survival Probabilities and Size of Cohorts
2010 2020 2030 2040 2050 2060
Decade
5 10 15 20 25 30
%
Population > 65 (in % of Working Age Population)
Balanced budget condition (no debt in model) Medicare and Social Security will grow if fraction of old increases → needs to be financed Assumption:
Fix Medicare payroll tax at benchmark level of 2.9% → Medicare is part of the overall gov’t budget constraint → adjust τC to cover the extra Medicare spending Social security is self-financing (by assumption) → increase τSS
2010 2020 2030 2040 2050 2060 Medicare in %: 17.68 21.74 26.21 27.01 26.76 27.42
5.00 7.21 10.59 12.10 12.08 12.43
9.38 12.19 15.61 16.23 16.04 16.58 Medicare tax: τMed % 2.90 2.90 2.90 2.90 2.90 2.90
The fraction of insured workers is fairly constant at around 81 percent IHI share ↑
Higher survival prob.→ reason to invest more in health → makes having IHI more desirable Marginal low risk types join → premiums ↓ 4 percent compared to the benchmark 2040 is different: A high risk group type collapses and produces many uninsured in that age/health cohort → IHI market shrinks
GHI share ↓
Increased premiums in GHI market around 2040 → drop in coverage to 76 The shrinking + aging causes a worsening of the GHI risk sharing pool → GHI premiums ↑
Medicaid ↑ because FPL is tied to median income
2010 2020 2030 2040 2050 2060 IHI in %: 6.43 13.06 10.71 7.39 10.04 10.70 GHI in %: 61.02 62.56 60.05 56.96 59.29 59.27 Medicaid in %: 9.78 10.20 11.56 12.01 11.39 11.42 Workers Insured %: 77.23 85.81 82.33 76.36 80.71 81.39
25 30 35 40 45 50 55 60 Age 20 40 60 80 100 %
[1] IHI %
2010 2040 2060 25 30 35 40 45 50 55 60 Age 20 40 60 80 100 %
[2] GHI %
2010 2040 2060 25 30 35 40 45 50 55 60 Age 20 40 60 80 100 %
[3] Medicaid %
2010 2040 2060
Insurance Take-Up: Aging
Retirees face larger health shocks More retirees → more medical spending However, aging causes private insurance premiums↓ as individuals become healthier → longer optimization horizon
2010 2020 2030 2040 2050 2060
100.00 118.28 131.61 138.26 141.15 144.13
100.00 114.58 125.73 132.31 134.35 136.95
100.00 69.87 80.90 100.27 85.66 84.96
100.00 170.05 131.16 98.14 131.46 134.75
100.00 106.41 98.16 95.45 99.84 100.56
100.00 110.78 118.26 121.58 119.21 120.93
100.00 132.48 166.84 181.55 184.92 190.45
Average worker is older → earning a higher level of labor income Decrease in workers →restricts the supply of labor → wages↑ Older households hold more assets/capital which increases the supply
Shift funds from general household consumption into the consumption
Medical sector grows
2010 2020 2030 2040 2050 2060 GDP: 100.00 105.50 101.73 101.20 103.86 105.27 Output: Yc 100.00 103.75 97.68 96.17 98.79 99.99 Output: pmYm 100.00 118.50 131.88 138.58 141.55 144.60 Capital: Kc 100.00 105.58 99.64 98.31 101.50 103.14 Capital: Km 100.00 120.59 134.53 141.66 145.43 149.15 Health capital: H 100.00 110.06 111.48 110.85 112.55 114.44 Human capital: HKc 100.00 102.87 96.73 95.14 97.48 98.47 Human capital: HKm 100.00 117.48 130.59 137.09 139.68 142.40 Consumption: C 100.00 104.18 97.30 95.17 97.33 97.90
100.00 118.28 131.61 138.26 141.15 144.13
Medicaid Expansion: eligibility threshold to 133 percent of the FPL and remove asset test Subsidies: Income is between 133 and 400 percent of the FPL are eligible to buy health insurance through insurance exchanges at subsidized rates according to
subj = max
j
− 0.03˜ yj
yj < 1.5 FPLMaid max
j
− 0.04˜ yj
yj < 2.0 FPLMaid max
j
− 0.06˜ yj
yj < 2.5 FPLMaid max
j
− 0.08˜ yj
yj < 3.0 FPLMaid max
j
− 0.095˜ yj
yj < 4.0 FPLMaid
Penalties: penaltyj = 1[insj+1=0] × 0.025 × ˜ yj,
Screening: Restrictions on the price setting and screening procedures
Financing: New payroll taxes for individuals with incomes higher than $200,000 per year New household budget constraint with the ACA:
cj + (1 + g) aj+1 + oW (mj) +1{inj+1=1}premIHI + 1{inj+1=2}premGHI = yj + tSI
j − taxj − 1{inj+1=0}penaltyj + 1{inj+1=1}subsidyj − taxACA j
2010 ACA -2020 2030 2040 2050 2060 GDP: 100.00 104.15 100.44 100.10 102.69 104.08 Health capital: H 100.00 110.22 111.63 110.99 112.68 114.57 Consumption: C 100.00 101.44 94.62 92.69 94.79 95.35
100.00 120.37 133.37 139.90 142.86 145.79
100.00 113.20 123.63 129.09 131.92 134.52
100.00 17.10 18.45 18.41 18.56 18.88
100.00 209.54 191.74 189.41 195.26 195.35
100.00 106.48 99.65 98.75 101.46 101.87
100.00 202.12 196.87 196.91 201.43 204.96
100.00 132.49 166.86 181.62 185.00 190.51
2010 ACA -2020 2030 2040 2050 2060 IHI in %: 6.43 21.71 21.14 20.98 21.05 20.94 GHI in %: 61.02 61.70 61.18 61.11 61.13 60.93 Medicaid in %: 9.78 16.10 16.92 17.12 16.99 17.20 Workers Insured %: 77.23 99.52 99.24 99.22 99.17 99.07 Medicare in %: 17.68 21.74 26.21 27.01 26.76 27.42
5.00 7.68 11.16 12.68 12.60 12.87
9.38 12.25 15.69 16.35 16.14 16.70 Medicare tax: τMed % 2.90 2.90 2.90 2.90 2.90 2.90 Payroll tax: τ V % 0.00 1.33 1.38 1.38 1.36 1.36
Isolate the net effects of the ACA reform different age profiles (Table: Aging & ACA in year t) - (Table: Aging-only in t )
ACA increases the social security tax Medical spending of the old increases slightly due to ACA
%∆ ACA - 2020 2030 2040 2050 2060 %∆ : M. sp.: Old 0.01 0.01 0.04 0.04 0.03 %∆ : Cons. tax: τ C % 0.47 0.57 0.58 0.52 0.44 %∆ : Soc. sec. tax: τSS % 0.07 0.08 0.11 0.10 0.11 %∆ : Medicare tax: τMed % 0.0 0.0 0.0 0.0 0.0 %∆ : Payroll tax: τ V % 1.33 1.38 1.38 1.36 1.36
Net impact of the ACA reform is a 18 percent rise in worker insurance take-up Driven almost entirely by increase in Medicaid and IHI participation GHI is relatively stable around 60 percent ACA ’prevents’ the drop in GHI in 2040 (without ACA)
%∆ ACA - 2020 2030 2040 2050 2060 %∆ : IHI in %: 8.65 10.42 13.60 11.02 10.24 %∆ : GHI in %:
1.13 4.16 1.84 1.66 %∆ : Medicaid in %: 5.91 5.36 5.11 5.60 5.78 %∆ : Workers Insured %: 13.71 16.91 22.86 18.46 17.68
2010 2020 2030 2040 2050 2060 20 40 60 80 100 Insurance Take-Up in %
noACA ACA noACA ACA noACA ACA noACA ACA noACA ACA noACA ACA
GHI IHI Medicaid
25 30 35 40 45 50 55 60 Age 20 40 60 80 100 %
[1] IHI %
2010 2060 2060-ACA 25 30 35 40 45 50 55 60 Age 20 40 60 80 100 %
[2] GHI %
2010 2060 2060-ACA 25 30 35 40 45 50 55 60 Age 20 40 60 80 100 %
[3] Medicaid %
2010 2060 2060-ACA
Insurance Take-Up: Aging + ACA
Level variables are normalized: (Table: Aging & ACA in year t) - (Table: Aging-only in t ) (Table: Aging-only in year t ) × 100
Aggregate health spending drops by a small percentage Uninsured individuals into insurance markets where prices paid for medical services are lower Substantial increase in spending from both Medicaid and IHI participants Increase in IHI → shifts in spending types within IHI
Subsidies → cause high risk types to enter into IHI IHI premiums increase about 20 percent
Total number of uninsured workers is much lower under the ACA As the population ages, the ability of the ACA to insure additional workers diminishes
With older age structure more individuals are covered by Medicare →limits the net effect of ACA
2020 2030 2040 2050 2060
1.77 1.34 1.18 1.21 1.15
23.22 46.19 92.99 48.53 44.97
0.07 1.51 3.45 1.62 1.30
82.46 66.48 61.96 68.97 69.49
0.01 0.01 0.04 0.04 0.03 pmM/ GDP % 0.01
ACA causes GDP ↓
Higher taxes: τC,τV Sector re-allocations:
Capital in non-medical sector ↓ 1 percent Capital in the medical sector ↑ 2 percent
Also τC↑ so that M ↑ and C ↓ → distortion Overall health H ↑
2020 2030 2040 2050 2060 GDP:
Health capital: H 0.15 0.13 0.12 0.12 0.12 Consumption: C
1.77 1.34 1.18 1.21 1.15
1 Construct a heterogeneous agents macro-model with health as a
durable good
2 Account for lifecycle patterns of health expenditures and private
insurance take up rates
3 Quantify the macroeconomic and distributional effects of aging and the
ACA
1 Relax some assumptions
Endogenize survival probability → affects assets accumulation
2 Additional experiments
Push Medicare eligibility to 66, 67, etc. Increase/decrease public insurance eligibility in current US system
Aiyagari, Rao S. 1994. “Uninsured Idiosyncratic Risk and Aggregate Saving.” The Quarterly Journal of Economics 109(3):659–684. Auerbach, Alan J. and William G. Gale. 2013. “Fiscal Myopia.” Working Paper . Auerbach, J. Alan and Laurence J. Kotlikoff. 1987. Dynamic Fiscal Policy. Cambridge: Cambridge University Press. Bewley, Truman. 1986. Stationary Monetary Equilibrium with a Continuum
Andreu Mas-Colell (Eds.), Contributions to Mathematical Economics in Honor of Gerard Debreu. North-Holland. Bloom, David E, David Canning and Günther Fink. 2010. “Implications of population ageing for economic growth.” Oxford Review of Economic Policy 26(4):583–612.
Boards of Trustees. 2015. “2015 Annual Report of the Boards of Trustees of the Federal Hospital Insurance and Federal Supplementary Medical Insurance Trust Funds.” The Boards of Trustees, Federal Hospital Insurance and Federal Supplementary Medical Insurance Trust Funds . Braun, A. and D. Joines. 2015. “The Implications of a Graying Japan for Government Policy.” Journal of Economic Dynamics & Control 57:1–23. Brown, Paul. 2006. Paying the Price: The High Cost of Prescription Drugs for Uninsured Californians. CALPIRG Education Fund. Capatina, Elena. 2015. “Life-Cycle Effects of Health Risk.” Journal of Monetary Economics 74:67–88.
Congressional Budget Office. De la Croix, David. 2013. Fertility, Education, Growth and Sustainability. Cambridge University Press.
De Nardi, Mariacristina, Selahattin Imrohoroğlu and Thomas J Sargent.
Economic Dynamics 2(3):575–615. Faruqee, Hamid. 2002. Population aging and its macroeconomic implications: A framework for analysis. Vol. 2 International Monetary Fund. Fonseca, Raquel, Pierre-Carl Michaud, Titus Galama and Arie Kapteyn.
Hall, Robert E. and Charles I. Jones. 2007. “The Value of Life and the Rise in Health Spending.” Quarterly Journal of Economics 122(1):39–72. Hubbard, Glenn R., Jonathan Skinner and Stephen P. Zeldes. 1995. “Precautionary Saving and Social Insurance.” The Journal of Political Economy 103(2):369–399. Huggett, Mark. 1993. “TheRisk-Free Rate in Heterogeneous-Agent Incomplete-Insurance Economies.” The Journal of Economic Dynamics and Control 17(5-6):953–969.
Janicki, Hubert. 2014. “The Role of Asset Testing in Public Health Insurance Reform.” Journal of Economic Dynamics and Control 44:169–195. Jeske, Karsten and Sagiri Kitao. 2009. “U.S. Tax Policy and Health Insurance Demand: Can a Regressive Policy Improve Welfare?” Journal of Monetary Economics 56(2):210–221. Jung, Juergen and Chung Tran. 2016. “Market Inefficiency, Insurance Mandate and Welfare: U.S. Health Care Reform 2010.” Review of Economic Dynamics 20:132–159. Kitao, S. 2015. “Fiscal Cost of Demographic Transition in Japan.” Journal
Kopecky, Karen A and Tatyana Koreshkova. 2014. “The Impact of Medical and Nursing Home Expenses on Savings.” American Economic Journal: Macroeconomics 6(3):29–72. Kotlikoff, Laurence J. 1988. Health Expenditures and Precautionary
Kotlikoff, Laurence J, Kent Smetters and Jan Walliser. 2007. “Mitigating America’s demographic dilemma by pre-funding social security.” Journal of Monetary Economics 54(2):247–266. Levin, Laurence. 1995. “Demand for Health Insurance and Precautionary Motive for Savings Among TheElderly.” Journal of Public Economics 57:337–367. Nishiyama, Shinichi. 2015. “Fiscal Policy Effects in a Heterogeneous-Agent OLG Economy with an Aging Population.” Journal of Economic Dynamics and Control 61(C):114–132. Palumbo, Michael G. 1999. “Uncertain Medical Expenses and Precautionary Saving Near the End of the Life Cycle.” Review of Economic Studies 66(2):395–421. Pashchenko, Svetlana and Ponpoje Porapakkarm. 2013. “Quantitative Analysis of Health Insurance Reform: Separating Regulation from Redistribution.” Review of Economic Dynamics 16 (3):383–404.
Phelps, Charles E. 2003. Health Economics. Upper Saddle River, New Jersey: Pearson Education Inc. Suen, Richard M. H. 2006. “Technological Advance and the Growth in Health Care Spending.” Economie D’Avant Garde, Research Report No.
Wise, David A. 2005. “Facing the Age Wave and Economic Policy: Fixing Public Pension Systems WithHealthcarein the Wings.” Fiscal Studies 26:5–34. Zhao, Kai. 2014. “Social Security and the Rise in Health Spending.” Journal
V (xj) = max
{cj,lj,mj,aj+1,inj+1}
j, εh j , εGHI j
(1)
cj + (1 + g) aj+1 + o (mj) + 1{inj+1=1}premIHI (j, h) + 1{inj+1=2 = yW
j
− taxj + tSI
j ,
≤ aj+1, 0 ≤ lj ≤ 1,
hj = i
j
yW
j
= e
j
+ profits, taxj = ˜ τ
yW
j
j
+ taxMcare
j
, ˜ yW
j
= yW
j
− aj − tBeq − 1[inj+1=2]premGHI − 0.5
j
+ taxMed
j
taxSS
j
= τ Soc × min
yss, e
j
, taxMcare
j
= τ Mcare ×
j
, tSI
j
= max
j
V (xj) = max
{cj,mj,aj+1}
j
s.t.
cj + (1 + g) aj+1 + γMcare × pMcare
m
× mj + premMcare = R
j
j
+ tSI
j ,
aj+1 ≥ 0,
where
taxj = ˜ τ
yR
j
˜ yR
j
= tSoc
j
+ r ×
j
tSI
j
= max
m
× mj + taxj − R
j
j
j,h
µj 1[inj(xj)=1]
pIHI
m mj,h (xj,h)
= R
J1−1
µj 1[inj,h(xj,h)=1]premIHI (j, h)
J1
µj 1[inj(xj)=2]
pGHI
m mj (xj)
= R
J1−1
µj 1[inj(xj)=2]premGHI dΛ (xj) ,
Back to Remaining Parts
G + T SI + T Med =
J
µj τ Cc (xj) + taxj (xj)
where T SI =
J
µj
j (xj) dΛ (xj)
T Med =
J
µj 1 − ρMed pMed
m
mj (xj) dΛ (xj) −
J
µj
Pensions:
J
µj
j
(xj) dΛ (xj) =
J1
µj
Accidental Bequests:
J1
µj
j
(xj) dΛ (xj) =
J
µjaj (xj) dΛ (xj)
Back to Remaining Parts
Given
j, Πh j , ΠGHI j,ϑ
J
j=1, {πj}J j=1 and
j=1 ,
a competitive equilibrium is a collection of sequences of: distributions {µj, Λj (xj)}J
j=1
individual household decisions {cj (xj) , lj (xj) , aj+1 (xj) , mj (xj) , inj+1 (xj)}J
j=1
aggregate stocks of capital and labor {K, L, Km, Lm} factor prices {w, q, R, pm} markups
and insurance premiums
J
j=1
such that:
(a) {cj (xj) , ll (xj) , aj+1 (xj) , mj (xj) , inj+1 (xj)}J
j=1
solves the consumer problem (b) the firm first order conditions hold: w = FL (K, L) = pmFm,L (Km, Lm) q = FK (K, L) = pmFm,K (Km, Lm) R = q + 1 − δ (c) markets clear
K + Km =
J
µj
J
µjaj (xj) dΛ (xj) +
J1−1
µj 1[inj+1=2] (xj) × premIHI (j, h)
+
J1−1
µj 1[inj+1=3] (xj) × premGHI dΛ (xj)
T Beq =
J
µjaj (xj) dΛ (xj) L + Lm =
J1
µj
(d) the aggregate resource constraint holds
G + (1 + g) S +
J
µj c (xj) + p
inj(xj) m
m (xj)
(e) the government programs clear (f ) the budget conditions of the insurance companies hold, and (g) the distribution is stationary (µj+1, Λ (xj+1)) = Tµ,Λ (µj, Λ (xj)) , where Tµ,Λ is a one period transition operator
Back to Competitive Equilibrium
Human capital: e = ej
= ǫl ×
χ ×
hj,ϑ
1−χ
wagej,ϑ from MEPS ǫl and Πl from prior studies using Tauchen (1986) procedure
Back to Health Shock
Offer shock: ǫGHI = {0, 1} where
0 indicates no offer and 1 indicates a group insurance offer
MEPS variables OFFER31X, OFFER42X, and OFFER53X Probability of a GHI offer is highly correlated with income Πh
j,ϑ with elements Pr
j+1|ǫGHI j
, ϑ
Coinsurance rates from MEPS Premiums clear insurance constraints Markup profits of GHI are zero Markup profits of IHI are calibrated to match IHI take up rate IHI profits used to cross-subsidize GHI
L is average/aggregate effective human capital and w × L average wage income Pension payments: tSoc (ϑ) = Ψ (ϑ) × w × L where Ψ (ϑ) is replacement rate that determines the size of pension payments Total pension amount to 4.1 percent of GDP
Premium for medicare at 2.11% of GDP (Jeske and Kitao (2009)) Coinsurance rates for Medicare and Medicaid from MEPS Calibrated: Medicaid eligibility FPLMaid at 60% of FPL to match % on Medicaid Calibrated: Asset test for Medicaid to match Medicaid take-up profile
Gouveia and Strauss (1994) for federal progressive income tax ˜ τ (˜ y) = a0
y −
˜
y−a1 + a2
−1/a1
Medicare tax is 2.9% Social security tax is 9% Consumption tax is 5%
Parameters: Explanation/Source:
J1 = 9
J2 = 6
n = 1.2% CMS 2010
years = 75 from age 20 to 95
A = 1 Normalization
α = 0.33 KydlandPescott1982
αm = 0.26 Donahoe (2000)
δ = 10% KydlandPescott1982
δh,j = [0.6% − 2.13%] MEPS 1999/2009
πj CMS 2010
see appendix
MEPS 1999/2009
see appendix
MEPS 1999/2009
see appendix
MEPS 1999/2009
see appendix
MEPS 1999/2009
see appendix
MEPS 1999/2009
Parameters: Explanation/Source:
σ = 3.0 to match K
Y and R
η = 0.43 to match labor supply and p×M
Y
ηm = 1.5 to match health capital profile
κ = 0.89 to match labor supply and p×M
Y
β = 1.0 to match K
Y and R
φj ∈ [0.7 − 0.99] to match spending profile
Am = 0.4 to match p×M
Y
ξ = 0.175 to match p×M
Y
χ = 0.26 to match labor supply
θ = 1 used for sensitivity analysis
Ψ = 40% to match τ soc
∆C = 12.0% to match size of tax revenue
hmin = 0.01 to match health spending
Back to Calibration